The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder

Background A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. Methods Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. Results We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = −0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = −0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = −0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error–corrected p < .05). Conclusions Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.


Parts of this Supplementary Online Content have been adapted from a previously
published one in Neuroimage:Clinical (doi: 10.1016/j.nicl.2023.103453).

Additional exclusion criteria
General exclusion criteria were: previous prescription of mirtazapine or vortioxetine at therapeutic dose, MRI contraindications, currently receiving specialist psychiatric treatment, high suicide risk on the Mini International Neuropsychiatric Interview (MINI) suicidality screen (1), past diagnosis of schizophrenia or schizo-affective disorder, psychotic symptoms using clinical screening questions, bipolar disorder, at risk of being violent, drug or alcohol abuse over the last six months, suspected neurological condition, pregnancy or insufficient contraception in women of childbearing age and breastfeeding or within six months of giving birth.

Recruitment and clinical assessment
We recruited participants from September 2018 to March 2020 partly through a clusterrandomized feasibility clinical trial, the Antidepressant Advisor Study (ADeSS; NCT03628027).Recruitment was halted due to the COVID-19 pandemic and recommenced in October 2020, using online advertising only, and was completed in August 2021.
As described in the trial protocol (2), general practitioner (GP) practices screened for patients with a history of treatment-resistance to antidepressant medications within their practice, i.e. non-responders to at least two serotonergic antidepressants in the current or previous episodes.Potential participants were approached for consent and, if given, asked to fill in a pre-screening questionnaire.Potentially eligible participants were invited for an indepth assessment by the study team, which included a clinical assessment using the Structured Clinical Interview for DSM-5 (SCID) to establish a current major depressive disorder (MDD) (3), a history of participants' depressive episodes, their current and past antidepressant medications, and completing various clinical, behavioral and experimental measures.
A follow-up assessment was conducted to establish whether any changes in baseline measures had occurred.This visit took place around 14-18 weeks after enrolling in the study, which should allow observation of any treatment effect if there is one.The assessment included questions related to medications taken in the study period as well as various clinical and behavioral measures.Please refer to the ADeSS trial protocol for more details regarding these procedures (2,4).
As the ADeSS trial was stopped due to the COVID-19 pandemic, an alternative recruitment route was employed to continue recruitment for the observational fMRI study.Trial adverts were posted online, with further dissemination of study adverts via university and institutional recruitment circulars.Interested participants were asked to complete a similar prescreening questionnaire as those approached for the ADeSS trial.If potentially eligible, participants were invited for an in-depth assessment to confirm their eligibility.For more details, please see Fennema (4).
A total of 1,755 participants with a history of MDD showed interest in participating and completed a pre-screening questionnaire.Potentially eligible MDD participants (n = 89) for the ADeSS trial and the fMRI study were invited to attend an in-depth assessment.Of those, 45 participants enrolled in the fMRI study, attended their MRI session and completed the study.
Of those 45 participants, ten participants were also part of the ADeSS trial (support tool arm: n = 4; treatment-as-usual arm: n = 6).
Upon study completion, participants in the MDD group were asked to refer partners or friends who might be interested in serving as control participants.Moreover, trial adverts were posted online, with further dissemination of study adverts via university and institutional recruitment circulars.Interested participants were asked to complete a pre-screening questionnaire targeted to control participants.If potentially eligible, participants were invited for an in-depth assessment to confirm their eligibility and they completed a similar battery of clinical, behavioral and experimental measures as the MDD group.
A total of 350 control participants completed a pre-screening questionnaire, with n = 113 meeting the initial eligibility criteria.Twenty-four control participants were invited for the initial baseline.Following the assessment, n = 22 control participants were enrolled in the study (n = 3 referred by a participant in the MDD group) and n = 20 control participants attended their MRI session.

Imaging criteria
For the primary analysis, all participants met strict criteria for signal dropout (sufficient coverage of the bilateral subgenual cortex, dorsal midbrain, left ventrolateral prefrontal cortex/insula and left ventromedial prefrontal cortex), movement (translation < 3mm; rotation < 2 degrees; less than 10% censored volumes based on framewise signal intensity [> 3 standard deviations from global mean] and framewise displacement [> 1 mm]) and usable physiological input.We chose a threshold of 10% of motion-contaminated volumes as a trade-off between retaining patient data with reasonable quality and avoiding overfitting with too many scanning nulling regressors.

Sample size
A formal power calculation was difficult, with no previous study from which effect sizes could be drawn.As such, this study should be considered as a proof-of-concept for using fMRI to prospectively predict prognosis in MDD.If the neural signatures have at least 70% accuracy, a minimum of n = 44 MDD patients is required to achieve 85% power for a significant prediction of response (p = .05)compared to chance (50%) using a binomial test.Even though a clinically relevant biomarker should show at least 80% accuracy (5), the proposed sample size is sufficient to determine the feasibility in a subsequent larger sample.

𝜎𝜎 𝑁𝑁
where  ̅ is the mean activation signal of the fMRI time series and   the standard deviation of the noise in the time series.Raw values were extracted using the MarsBaR toolbox ( 6 While in the MRI scanner, the participant's head motion was restricted using padding, and heart rate and respiration rate measurements were recorded via a manufacturer-supplied finger pulse sensor (peripheral plethysmograph) and respiratory belt, respectively.A mirror fitted to the head coil allowed participants to view visual stimuli presented during image acquisition, as stimuli were projected onto a screen located behind the participant's head.
Verbal instructions were communicated via the MRI intercom, using a pre-defined script to ensure consistency between participants.After the scanning session, participants were asked what they thought about when letting their mind wander.

Image analysis
Functional (second order) and cardio-respiratory interactions (first order) (11).Higher orders of Fourier expansions have been recommended to optimize physiological denoising (12), but this would have resulted in overfitting of the model due to the large number of regressors and limited number of volumes in the current study.The settings used in our study were in line with the original RETROICOR method (11) and represented a trade-off between denoising and overfitting.

Behavioral data analysis
Data were checked for outliers using standardized scores (outside z = ± 2.5 standard deviations from the mean) for the major depressive disorder (MDD) group and the control group separately.Results with outliers were confirmed by supplementary analyses replacing the outlying value by the nearest occurring value in the rest of the sample that was not an outlier.
Moreover, data were screened for normal distribution within each group with Kolmogorov-Smirnov tests and if the assumption of normality was violated, non-parametric Mann-Whitney-U tests instead of independent sample t-tests were used to investigate between-group differences (MDD vs controls).

Moral sentiment and action tendencies task
In addition to the standard tests, participants completed an experimental, computerized cognitive task, which investigates the neurocognitive underpinnings of blame-related emotions: the moral sentiment and action tendencies task (MSAT).This task has been validated in previous studies (13)(14)(15), but here, we used the modified, shortened version as described in Duan et al. (16) and Fennema et al. (17).
In short, participants were shown written statements describing actions counter to social and moral values, using Excel Macro or using an online-based version on PsychoPy (18).They were asked to select the emotion that best described how they would feel given the unpleasant hypothetical situation: guilt, shame, contempt/disgust towards self, contempt/disgust towards friend, indignation/anger towards friend, or no feeling/other feeling.Moreover, they were asked to select the action they would most strongly feel like doing: creating distance from self, hiding, apologizing, creating distance from friend, verbally or physically attacking/punishing friend, or no action/other action.Lastly, participants had to indicate how strongly they would blame themselves (i.e.self-blame rating) and how strongly they would blame their friend (i.e. other-blame rating) for the imagined behavior, using a 7-point visual analogue scale, where 1 = not at all and 7 = very much.
MSAT data were checked for completeness, i.e. number of trials in which the participant did not select at least one moral emotion/action tendency.Even though participants were instructed to restrict their choice to only one moral emotion/action tendency, some participants selected more than one choice and such MSAT trials were excluded from the analysis.Participants with less than 80% valid trials were excluded from the overall analysis (n = 4/43).

Exploratory cross-sectional fMRI findings
The two-sample SPM model probing group effects (MDD vs controls) did not show any differences in connectivity with the subgenual cortex seed region, using small-volume correction with our a priori ROIs (left ventrolateral prefrontal cortex/insula, left ventromedial prefrontal cortex and dorsal midbrain).These null findings were confirmed for the extracted

Supplementary Tables
Table S1 | Overview of inclusion / exclusion for imaging analysis.

Figure S4 |
Figure S4 | Voxel-based analysis showing association between change in depressive score MATLAB PhysIO toolbox was used to partially mitigate the impact of physiological noise (9) (version R2021a-v8.0.0, open-source code available as part of the Translational Algorithms for Psychiatry-Advancing Science [TAPAS] software collection (10): https://www.translationalneuromodeling.org/tapas).Heart rate and respiration rate measurements were used in a retrospective image correction (RETROICOR) model, using Fourier expansions of different orders for the estimated phases of cardiac pulsation (second order), respiration resting-state echo-planar images (EPIs) and IR-SPGR anatomical images were preprocessed in Data Processing Assistant for Resting-State fMRI Advanced Edition (DPARSF; http://rfmri.org/DPARSFl)(8), i.e. applying slice timing correction, spatial realignment, coregistration of anatomical images to the EPIs, segmentation, normalization (resliced at a voxel size of 3 x 3 x 3 mm) and smoothing, using a kernel of full-width half-maximum equal to 6 mm.Artifact Detection Tools (ART; http://web.mit.edu/swg/software.htm) was used to flag spikes in motion, i.e. framewise signal intensity > 3 standard deviation from the global mean and framewise head displacement > 1 mm, and to create nulling regressors.Participants with spikes in more than 10% of the functional images were deemed to have moved too much and were excluded from the analysis.There is no fixed rule for proportion of spikes above which data should be rejected, but this allowed for a trade-off between retaining patient data with reasonable quality and avoiding overfitting with too many scan-nulling regressors.In addition, the

Table S4 | Movement parameters and content of mind wandering during resting-state scan by group.
Participants were asked to complete a short questionnaire after the fMRI session, to collect ratings on mind wandering during the resting-state scan.b Number of elements chosen, excluding the "none of the above" option.Means and standard deviations are reported (M ± SD; maximum -minimum).Percentages may not add up to 100 due to rounding.* significant at p < .05threshold, two-tailed.MDD = major depressive disorder; RMS = root mean square. a

Table S5 | Baseline clinical characteristics control participants (n=16). This table has been adapted from a previously published one in Neuroimage:Clinical (doi: 10.1016/j.nicl.2023.103453) Past depressive symptoms not meeting MDE criteria
Percentages may not add up to 100 due to rounding.MDD = major depressive disorder; MDE = major depressive episode; DSM-5 = Diagnostic and Statistical Manual for Mental Disorders 5 th edition.